| tf101 | Deep Learning with TensorFlow | 21 hours | TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.
Audience
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
After completing this course, delegates will:
- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, building graphs and logging
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| tfir | TensorFlow for Image Recognition | 28 hours | This course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition
Audience
This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition
After completing this course, delegates will be able to:
- understand TensorFlow’s structure and deployment mechanisms
- carry out installation / production environment / architecture tasks and configuration
- assess code quality, perform debugging, monitoring
- implement advanced production like training models, building graphs and logging
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| tsflw2v | Natural Language Processing with TensorFlow | 35 hours | TensorFlow™ is an open source software library for numerical computation using data flow graphs.
SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.
Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).
Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.
Audience
This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.
After completing this course, delegates will:
- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, embedding terms, building graphs and logging
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| dlv | Deep Learning for Vision | 21 hours | Audience
This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source ) for analyzing computer images
This course provide working examples.
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| Neuralnettf | Neural Networks Fundamentals using TensorFlow as Example | 28 hours | This course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).
This training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
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| tpuprogramming | TPU Programming: Building Neural Network Applications on Tensor Processing Units | 7 hours | The Tensor Processing Unit (TPU) is the architecture which Google has used internally for several years, and is just now becoming available for use by the general public. It includes several optimizations specifically for use in neural networks, including streamlined matrix multiplication, and 8-bit integers instead of 16-bit in order to return appropriate levels of precision.
In this instructor-led, live training, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.
By the end of the training, participants will be able to:
- Train various types of neural networks on large amounts of data
- Use TPUs to speed up the inference process by up to two orders of magnitude
- Utilize TPUs to process intensive applications such as image search, cloud vision and photos
Audience
- Developers
- Researchers
- Engineers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
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| embeddingprojector | Embedding Projector: Visualizing your Training Data | 14 hours | Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.
This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.
By the end of this training, participants will be able to:
- Explore how data is being interpreted by machine learning models
- Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
- Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
- Explore the properties of a specific embedding to understand the behavior of a model
- Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
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| tensorflowserving | TensorFlow Serving | 7 hours | TensorFlow Serving is a system for serving machine learning (ML) models to production.
In this instructor-led, live training, participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.
By the end of this training, participants will be able to:
- Train, export and serve various TensorFlow models
- Test and deploy algorithms using a single architecture and set of APIs
- Extend TensorFlow Serving to serve other types of models beyond TensorFlow models
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
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| undnn | Understanding Deep Neural Networks | 35 hours | This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).
Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.
Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.
Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.
Audience
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
After completing this course, delegates will:
-
have a good understanding on deep neural networks(DNN), CNN and RNN
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understand TensorFlow’s structure and deployment mechanisms
-
be able to carry out installation / production environment / architecture tasks and configuration
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be able to assess code quality, perform debugging, monitoring
-
be able to implement advanced production like training models, building graphs and logging
Not all the topics would be covered in a public classroom with 35 hours duration due to the vastness of the subject.
The Duration of the complete course will be around 70 hours and not 35 hours.
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| dlfornlp | Deep Learning for NLP (Natural Language Processing) | 28 hours | Deep Learning for NLP allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP.
In this instructor-led, live training, participants will learn to use Python libraries for NLP (Natural Language Processing) as they create an application that processes a set of pictures and generates captions.
By the end of this training, participants will be able to:
- Design and code DL for NLP using Python libraries
- Create Python code that reads a substantially huge collection of pictures and generates keywords
- Create Python Code that generates captions from the detected keywords
Audience
- Programmers with interest in linguistics
- Programmers who seek an understanding of NLP (Natural Language Processing)
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
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